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Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue

Paper · 2026 · Sukannya Purkayastha, Nils Dycke, Anne Lauscher et al.

Summary

The paper reframes meta-reviewing as a decision-making task rather than a summarization task. In this view, a dialogue agent answers the meta-reviewer's questions about the reviews. It can contrast reviewer opinions, help weigh arguments and place them in context, but it never volunteers verdicts. The authors set two requirements for such an agent: GROUNDEDNESS (answers stay faithful to the reviews) and SPECIFICITY (answers are technical, not generic). Off-the-shelf LLMs meet neither requirement, and expert-written dialogues are expensive to collect. To get training data, the authors therefore generate synthetic dialogues with LLMs and improve them through ReMuSE, a self-refinement loop that uses feedback on several quality aspects at once. Smaller open-source models fine-tuned on this data beat zero-shot ChatGPT in both automatic and human evaluation. In a within-subject user study, the best agent cut meta-reviewing time by close to half and led to more thorough meta-reviews. The work points to a human-centred form of AI support for peer review that leaves the final decision with the human.

Key points

  • ReMuSE (Reward-based Multi-aspect Self-Editing) extends earlier self-refinement methods, which corrected only one aspect or depended on gold in-context examples. The authors report that it also generalizes to other decision-making domains.
  • Synthetic dialogues were produced with ChatGPT, LLaMa, Mistral and Mixtral and grounded in peer reviews from ORSUM. Without refinement, the generated dialogues were vague and lacked variety.
  • In the user study, agent assistance reduced meta-reviewing time by almost 50% compared with working without an agent.
  • The design follows ACL policy, which discourages LLM ghostwriting: the agent only reacts to the user's questions and does not write meta-reviews.
  • Scope and code: the study covers English-language reviews from AI conferences, and the code is available at github.com/UKPLab/eacl2026-meta-review-as-dialog.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Technische Universität DarmstadtDarmstadt · Germany109 works · 35,920 citations🎓Universität HamburgHamburg · Germany79 works · 5,422 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Dialogue systems · 624 works

Cite

Cite as Sukannya Purkayastha, Nils Dycke, Anne Lauscher and Iryna Gurevych. 2026. Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.158

@inproceedings{purkayastha2026decision,
  title = {{Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue}},
  author = {Sukannya Purkayastha, Nils Dycke, Anne Lauscher and Iryna Gurevych},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.158},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.158}
}

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